An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique

Engineering Applications of Artificial Intelligence - Tập 26 - Trang 2105-2127 - 2013
Shahaboddin Shamshirband1,2, Nor Badrul Anuar1,2, Miss Laiha Mat Kiah1,2, Ahmed Patel3,4
1Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia
2Security Research Group (SECReg), University of Malaya, Kuala Lumpur, Malaysia
3School of Computer Science, Centre of Software Technology and Management (SOFTAM), Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
4School of Computing and Information Systems, Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames KT1 2EE, United Kingdom

Tài liệu tham khảo

Abadeh, 2007, Intrusion detection using a fuzzy genetics-based learning algorithm, J. Network Comput. Appl., 30, 414, 10.1016/j.jnca.2005.05.002 Abraham, 2007, D-SCIDS: distributed soft computing intrusion detection system, J. Network Comput. Appl., 30, 81, 10.1016/j.jnca.2005.06.001 Agah, 2007, Preventing DoS attacks in wireless sensor networks: a repeated game theory approach, Int. J. Network Secur., 5, 145 Agah, A., Das, S.K., Basu, K., Asadi, M., 2004. Intrusion detection in sensor networks: a non-cooperative game approach. In: Proceedings of Third IEEE International Symposium on Network Computing and Applications, IEEE, pp. 343–346. Ahmadabadi, 2001, Cooperative Q-learning: the knowledge sharing issue, Adv. Robotics, 15, 815, 10.1163/156855301317198142 Akyildiz, 2002, Wireless sensor networks: a survey, Comput. Networks, 38, 393, 10.1016/S1389-1286(01)00302-4 Alan Bivens, 2002, Network-based intrusion detection using neural networks, Intell. Eng. Syst. Artif. Neural Networks, 579 Alpadin, 2010 Andersen, 2009, Experiments with online reinforcement learning in real-time strategy games, Appl. Artif. Intell. Int. J., 23, 855, 10.1080/08839510903246526 Anderson, D., Lunt, T.F., Javitz, H., Tamaru, A., Valdes, A., 1995. Detecting Unusual Program Behavior Using the Statistical Component of the Next-generation Intrusion Detection Expert System (NIDES), SRI International Computer Science Laboratory. Anderson, 1995 Anuar, 2012, Incident prioritisation using analytic hierarchy process (AHP): Risk Index Model (RIM), Security Comm. Networks Aydın, 2009, A hybrid intrusion detection system design for computer network security, Comput. Electr. Eng., 35, 517, 10.1016/j.compeleceng.2008.12.005 Balajinath, 2001, Intrusion detection through learning behavior model, Comput. Commun., 24, 1202, 10.1016/S0140-3664(00)00364-9 Bankovic, 2011, Improving security in WMNs with reputation systems and self-organizing maps, J. Network Comput. Appl., 34, 455, 10.1016/j.jnca.2010.03.023 Barto, 1998 Bivens, A., Palagiri, C., Smith, R., Szymanski, B., Embrechts, M., 2002. Network-based intrusion detection using neural networks. In: Proceedings of the Intelligent Engineering Systems through Artificial Neural Networks, New York, pp. 579–584. Blasco, J., Orfila, A., Ribagorda, A., 2010. Improving network intrusion detection by means of Domain-Aware genetic programming. In: International Conference on Availability, Reliability, and Security, pp. 327–332. Bridges, S.M., Vaughn, R.B., 2000. Fuzzy data mining and genetic algorithms applied to intrusion detection. In: Twenty-third National Information Systems Security Conference, pp. 13–31. Buckley, 1994, Fuzzy genetic algorithm and applications, Fuzzy Sets Syst., 61, 129, 10.1016/0165-0114(94)90228-3 C-sniper, 2012. Counter-Sniper Program (C-sniper). Cannady, J., 1998. Artificial neural networks for misuse detection. In: Proceedings of the 1998 National Information Systems Security Conference (NISSC'98), Arlington, VA, pp. 443–456. Chavan, S., Shah, K., Dave, N., Mukherjee, S., Abraham, A., Sanyal, S., 2004. Adaptive neuro-fuzzy intrusion detection systems. In: Proceeding of International Conference on Information Technology: Coding and Computing, vol. 71, IEEE, pp. 70–74. Chen, 2002, Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks, Wireless Networks, 8, 481, 10.1023/A:1016542229220 Curiac, 2012, Ensemble based sensing anomaly detection in wireless sensor networks, Exp. Syst. Appl., 39, 9087, 10.1016/j.eswa.2012.02.036 Dasgupta, 2005, CIDS: an agent-based intrusion detection system, Comput. Secur., 24, 387, 10.1016/j.cose.2005.01.004 Dartmouth College, 2006. Sensor Network Dataset for Enhancing CSMA MAC Protocoi. Obtained from: http://crawdad.cs.dartmouth.edu/meta.php?name=columbia/ecsma. Davis, 2011, Data preprocessing for anomaly based network intrusion detection: a review, Comput. Secur., 30, 353, 10.1016/j.cose.2011.05.008 Debar, H.B., M.; Siboni, D., 1992. A neural network component for an intrusion detection system. In: IEEE Computer Society Symposium on Research in Security and Privacy IEEE, Oakland, CA, pp. 240–250. Denning, 1987, An intrusion-detection model, IEEE Trans. Software Eng., 13, 222, 10.1109/TSE.1987.232894 Devarakonda, 2012, Integrated Bayes network and hidden Markov model for host based IDS, Int. J. Comput. Appl., 41, 45 Dickerson, J.E., Juslin, J., Koukousoula, O., Dickerson, J.A., 2001. Fuzzy intrusion detection. In: Proceedings of the 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS), Atlanta, GA, pp. 1506–1510. Doelitzscher, 2012, An agent based business aware incident detection system for cloud environments, J. Cloud Comput. Adv. Syst. Appl., 1, 9, 10.1186/2192-113X-1-9 Dutkevych, T., Piskozub, A., Tymoshyk, N., 2007. Real-time intrusion prevention and anomaly analyze system for corporate networks. In: Fourth IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IEEE, pp. 599–602. Fisch, 2012, Learningfrom others: exchange of classification rules in intelligent distributed systems, Artif. Intell, 10.1016/j.artint.2012.04.002 Fragkiadakis, 2012, Design and performance evaluation of a lightweight wireless early warning intrusion detection prototype, J. Wireless Commun. Network, 2012, 1 Fuchsberger, A., 2005. Intrusion Detection Systems and Intrusion Prevention Systems, Information Security Technical Report, pp. 134–139. Fullér, 2000, 289 Garcia-Teodoro, 2009, Anomaly based network intrusion detection: Techniques, systems and challenges, Comput. Secur., 28, 18, 10.1016/j.cose.2008.08.003 Gomez, J., Dasgupta, D., 2002. Evolving fuzzy classifiers for intrusion detection. In: Proceedings of the 2002 IEEE Workshop on Information Assurance United States Military Academy. IEEE Computer Press, West Point, NY, pp. 321–323. Gu, G., Fogla, P., Dagon, D., Lee, W., Skori, B., 2006. Measuring intrusion detection capability: an information-theoretic approach. In: Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security. ACM, Taipei, Taiwan, pp. 90–101. Gunes Kayacik, 2007, A hierarchical SOM-based intrusion detection system, Eng. Appl. Artif. Intell., 20, 439, 10.1016/j.engappai.2006.09.005 Hanson, 2009, Body area sensor networks: challenges and opportunities, Computer, 42, 58, 10.1109/MC.2009.5 Herrero, 2009, MOVIH-IDS: a mobile-visualization hybrid intrusion detection system, Neurocomputing, 72, 2775, 10.1016/j.neucom.2008.12.033 Hofmeyr, 2000, Architecture for an artificial immune system, Evol. Comput., 8, 443, 10.1162/106365600568257 Huang, 2011, Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining, Inf. Sci., 231, 32, 10.1016/j.ins.2011.03.014 Idris, N.B., Shanmugam, B., 2005. Artificial intelligence techniques applied to intrusion detection. In: Annual IEEE INDICON, pp.52-55. Jianhui, 2008, A fast fuzzy set intrusion detection model, IEEE, 601 Jungwon, K., Bentley, P.J., 2001. Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1242, pp. 1244–1252. Kapitanova, 2012, Using fuzzy logic for robust event detection in wireless sensor networks, Ad Hoc Networks, 10, 709, 10.1016/j.adhoc.2011.06.008 KDD'99, 1999. KDD Cup 1999 Data. Khan, 2012, Application of fuzzy inference systems to detection of faults in wireless sensor networks, Neurocomputing, 94, 111, 10.1016/j.neucom.2012.04.002 Khanna, R., Huaping, L., Hsiao-Hwa, C., 2009. Reduced Complexity Intrusion Detection in Sensor Networks Using Genetic Algorithm, International Conference on Communications. IEEE, Dresden, pp. 1–5. Kolias, 2011, Swarm intelligence in intrusion detection: a survey, Comput. Secur., 30, 625, 10.1016/j.cose.2011.08.009 lab, I.B.R., 2004. Wireless Dataset. León, 2011, 231 Li, 2009, Privacy preservation in wireless sensor networks: a state-of-the-art survey, Ad Hoc Networks, 7, 1501, 10.1016/j.adhoc.2009.04.009 Li, 2012, Security through collaboration and trust in MANETs, Mobile Networks Appl., 17, 342, 10.1007/s11036-010-0243-9 Li, 2012, An efficient intrusion detection system based on support vector machines and gradually feature removal method, Exp. Syst. Appl., 39, 424, 10.1016/j.eswa.2011.07.032 Liang, Q., Wang, L., 2005. Event detection in wireless sensor networks using fuzzy logic system. In: Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, pp. 52–55. Liao, 2012, Intrusion detection system: a comprehensive review, J. Network Comput. Appl. Lin, J., Huang, T., Zhao, B., 2008. A fast fuzzy set intrusion detection model. In: International Symposium on Knowledge Acquisition and Modeling. IEEE, Wuhan, pp. 601–605. Lockhart, A., 2007. Snort Wireless, retrieved from the web. Ma, 2007, 60 Mohajerani, M., Moeini, A., Kianie, M., 2003. NFIDS: a neuro-fuzzy intrusion detection system. In: Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems, vol. 341. IEEE, pp. 348–351. Mosqueira-Rey, 2007, 466 Muñoz, 2013, Optimization of load balancing using fuzzy Q-learning for next generation wireless networks, Exp. Syst. Appl., 40, 984, 10.1016/j.eswa.2012.08.071 NSL-KDD, 2009. The NSL-KDD Data Set. Oblak, 2007, Fault detection for nonlinear systems with uncertain parameters based on the interval fuzzy model, Eng. Appl. Artif. Intell., 20, 503, 10.1016/j.engappai.2006.08.002 Patcha, 2007, An overview of anomaly detection techniques: existing solutions and latest technological trends, Comput. Networks, 51, 3448, 10.1016/j.comnet.2007.02.001 Patel, A., Taghavi, M., Bakhtiyari, K., Júnior J.C., Taxonomy and Proposed Architecture of Intrusion Detection and Prevention Systems for Cloud Computing, Cyberspace Safety and Security,Lecture Notes in Computer Science vol. 7672, 2012, pp. 441-458. 10.1007/978-3-642-35362-8_33. Patel, 2013, An intelligent collaborative intrusion detection and prevention system for smart grid environments, Comput. Standards Interfaces, 10.1016/j.csi.2013.01.003 Patel, 2013, An intrusion detection and prevention system in cloud computing: a systematic review, J. Network Comput. Appl., 36, 25, 10.1016/j.jnca.2012.08.007 Potyrailo, 2012, Wireless sensors and sensor networks for homeland security applications, J. Trac-Trend Anal. Chem., 10.1016/j.trac.2012.07.013 Qiming, H., Shayman, M.A., 2000. Using reinforcement learning for pro-active network fault management. In: International Conference on Communication Technology Proceedings, vol. 511. IEEE, Beijing, pp. 515–521. Ramachandran, 2008, FORK: A novel two-pronged strategy for an agent-based intrusion detection scheme in ad-hoc networks, Comput. Commun., 31, 3855, 10.1016/j.comcom.2008.04.012 Renjit, 2011, Multi-agent-based anomaly intrusion detection, Inf. Secur. J. Global Perspect., 20, 185, 10.1080/19393555.2011.589424 Russell, 2007 Saniee Abadeh, 2007, A parallel genetic local search algorithm for intrusion detection in computer networks, Eng. Appl. Artif. Intell., 20, 1058, 10.1016/j.engappai.2007.02.007 Schaffer, 2012, Secure and reliable clustering in wireless sensor networks: a critical survey, Comput. Networks, 56, 2726, 10.1016/j.comnet.2012.03.021 Şen, S., Clark, J.A., 2009. A grammatical evolution approach to intrusion detection on mobile ad hoc networks. In: Proceedings of the Second ACM Conference on Wireless Network Security. ACM, pp. 95–102. Sevil, 2011, Evolutionary computation techniques for intrusion detection in mobile ad hoc networks, Comput. Networks, 55, 3441, 10.1016/j.comnet.2011.07.001 Sherif, J.S., Dearmond, T.G., 2002. Intrusion detection: systems and models. In: Proceedings of Eleventh IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. IEEE, pp. 115–133. Simon, G., Maroti, M., Leczi, A., Balogh, G., Kusy, B., Nadas, A., Pap, G., Sallai, J., Frampton, K., 2004. Sensor network-based countersniper system. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems. ACM, Baltimore, MD, USA, pp. 1–12. Stafrace, 2010, Military tactics in agent-based sinkhole attack detection for wireless ad hoc networks, Comput. Commun., 33, 619, 10.1016/j.comcom.2009.11.006 T. Bokareva, W.H.S. Kanhere, B. Ristic, N. Gordon, T. Bessell, M. Rutten, S. Jha, 2006. Wireless sensor networks for battlefield surveillance. In: Proceeding of Land Warfare Conference (LWC), Brisbane, p. 8. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A., 2009. A Detailed Analysis of the KDD CUP 99 Data Set. Tong, W., Zhe, L., Chunhui, Z., 2009. A detection method for routing attacks of wireless sensor network based on fuzzy C-means clustering. In: Proceeding of Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, Tianjin, pp. 445–449. Toosi, 2007, A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers, Comput. Commun., 30, 2201, 10.1016/j.comcom.2007.05.002 Tsai, 2009, Intrusion detection by machine learning: a review, Exp. Syst. Appl., 36, 11994, 10.1016/j.eswa.2009.05.029 Vakili, 2011, Coordination of cooperation policies in a peer-to-peer system using swarm-based RL, J. Network Comput. Appl. Wang, 2011, An integrated intrusion detection system for cluster-based wireless sensor networks, Exp. Syst. Appl., 38, 15234, 10.1016/j.eswa.2011.05.076 Wang, Y.T., Bagrodia, R., 2012. ComSen: a detection system for identifying compromised nodes in wireless sensor networks. In: The Sixth International Conference on Emerging Security Information, Systems and Technologies, pp. 148–156. Wooldridge, 2009 Wu, 2010, The use of computational intelligence in intrusion detection systems: a review, Appl. Soft Comput., 10, 1, 10.1016/j.asoc.2009.06.019 Xu, 2010, Sequential anomaly detection based on temporal-difference learning: Principles, models and case studies, Appl. Soft Comput., 10, 859, 10.1016/j.asoc.2009.10.003 Xu, 2007, Defending DDoS attacks using hidden Markov models and cooperative reinforcement learning intelligence and security informatics, 196, 10.1007/978-3-540-71549-8_17 Xu, 2005, 995 Yan Li, W.J., 2012. The method of network intrusion detection based on the neural network GCBP algorithm. In: International Conference on Computer Science and Information Processing (CSIP). IEEE, pp. 1082–1086. Ye, N., 2000. A Markov chain model of temporal behavior for anomaly detection. In: Proceedings of the 2000 IEEE Workshop on Information Assurance and Security United States Military Academy. IEEE, West Point, NY, p. 169. Zadeh, 1994, Soft computing and fuzzy logic, IEEE Software, 11, 48, 10.1109/52.329401 Zhang, Y., Lee, W., 2000. Intrusion detection in wireless ad-hoc networks. In: Proceedings of the 6th Annual International Conference on Mobile Computing and Networking. ACM, Boston, Massachusetts, United States, pp. 275–283. Zhang, Z., Li, J., Manikopoulos, C., Jorgenson, J., Ucles, J., 2001. HIDE: a hierarchical network intrusion detection system using statistical preprocessing and neural network classification. In: Proceedings of the IEEE Workshop on Information Assurance and Security United States Military Academy. IEEE, West Point, NY, pp. 85–90.